Machine Learning-Based Prediction System for Risk Assessment of Hypertension Using Symptoms Investigations
Journal Title: International Journal of Experimental Research and Review - Year 2024, Vol 46, Issue 10
Abstract
Hypertension is a common condition of cardiovascular disease that poses significant health challenges among the public on a larger scale globally. It is important to accurately predict the risk of hypertension to save people and improve overall quality of life. Traditionally, the detection of hypertension relies on clinical criteria such as blood pressure measurement and examination of medical history. However, these methods have drawbacks involving potential human error, time consumption, and the possibility of missed diagnoses. The paper aims to identify the features or symptoms of hypertension disease and predict its risk factors using machine learning algorithms. Apart from this, it is of utmost importance to identify the symptoms as they play a pivotal role in recognizing the type of risk for hypertension. To successfully conduct the work, a dataset of 13 attributes, including gender, age, smoking habits, etc, has been used, which is further visualized graphically to understand the pattern among them. Later, multiple machine learning-based learning techniques have been applied and examined on the basis of standard metrics. Results indicate that random forest models outperform existing approaches, achieving an accuracy of 87.26% in predicting low and high-risk hypertension. Furthermore, classification reports reveal superior precision, recall, and F1-score for random forests compared to alternative models. Insights from learning curves and confusion matrices provide a valuable understanding of model performance and data sufficiency. Overall, this research highlights the impact of machine learning in accurately predicting the risk of hypertension and underscores the importance of ongoing research efforts to translate these findings into practical clinical applications.
Authors and Affiliations
Simranjit Kaur, Khushboo Bansal, Yogesh Kumar
Keywords
Related Articles
- EP ID EP754334
- DOI 10.52756/ijerr.2024.v46.011
- Views 21
- Downloads 0
Pervasiveness of work-related Musculoskeletal Disorders on Indian construction workers
Indian construction involves a large number of organized and unorganized workers. There is always a risk of accidents, injuries and disability due to working in an awful, inconvenient workplace and unfavourable condition...
ISM approach to model financial risks in Indian KPO organization
Globalization has entered a new phase due to significant advancements in technology. This progress has allowed organizations to reduce costs while enhancing their market responsiveness, thus gaining a competitive edge. N...
Influencer Marketing: A Citation and Co-citation Analysis within Business and Management Domains
Influencer marketing has emerged as a popular approach businesses use to incorporate social influence into their operations and marketing strategies. It seems to be a useful and cost-effective strategy. It ensures a broa...
Development and Validation of an ICH-Compliant Optimized RP-HPLC Method for Quantitative Analysis of Favipiravir
Favipiravir (FAV) has emerged as a promising antiviral agent. It is particularly effective against influenza and other RNA virus infections. The aim and objective of the present study was developing and optimizing chroma...
Distribution of heavy metals in the sediments of Hooghly, Jalangi and Churni river in the regions of Murshidabad and Nadia districts of West Bengal, India
The industrial outburst, widespread uses of pesticides and fertilizers, heavy sew-age effluents, domestic trash, and pharmaceutical waste through surface runoff have result-ed in large scale increase in metal conc...